As we review our strategy, we plan to share here much of what we’ve learned through programming in more than a dozen countries – from our work and from our excellent partners – about the state of data in agriculture, tobacco control, open contracting, and the extractive industries. For each theme, we’ll explore who are the key data users, the decisions they make, the most important data gaps, and the crucial risks of data (mis)use. Here we share previews from some of our flagship programs.
In developing the VIFAA Kenya Dashboard, we worked in partnership with Africafertilizer.org (AFO) and the International Fertilizer Development Center (IFDC) to understand the cycle of demand, supply, and use of Kenya’s fertilizer data. Grace Chilande of AFO and IDFC provides more information on why the dashboard is needed and how it will be used.
As the world continues to face the effects of Covid-19, policymakers are turning to data more than ever to understand the scope of the crisis, anticipate its spread, and formulate policy decisions; but gender-disaggregated data are missing from the picture. Knowing what information is being captured and what is not could impact decision-making.
From our experience understanding data use, the primary obstacle to measuring and organizational learning from feminist outcomes is that development actors do not always capture gender data systematically. What can be done to change that?
March is International Women’s History Month. Throughout the next weeks, DG will be publishing a series of blogs that highlight and honor the work that we and others are doing to support the vital role of women. We’re kicking off the series with this post, highlighting the importance of gender data.
Today, Development Gateway (DG) is pleased to announce the publication of the Managing for Feminist Results: Measuring Canada’s Feminist International Assistance Policy white paper, that outlines the challenges and opportunities that development agencies may face when adopting new and/or feminist policies.
Last month, we explored opportunities for better resourcing for the data revolution – and found that calls for greater coordination particularly resonated. Amongst development providers, there’s concern about duplication of efforts – particularly in a political context of decreasing budgets and increasing scrutiny for aid.
Data-driven decision-making was considered a positive norm across countries researched during the UNICEF Data for Children pilot process. Ideally, the national data use cycle would consist of: (i) evidence-based planning; (ii) implementing programs according to plans; (iii) monitoring and evaluation (M&E) and reporting; (iv) analyzing data; and (v) making appropriate planning or program adjustments.
We know that Monitoring and Evaluation (M&E) aims to support accountability and learning, in order to drive better outcomes. Through the Results Data Initiative (RDI), we’ve approached M&E with a critical eye – how can we better allocate M&E funding, and what results are we really striving for?
The 2030 Sustainable Development Agenda offers the promise of new technologies, big data sources, and increased government statistical capacity for data-driven policy and progress. However, we cannot assume that more data will result in a better world. Moreover, a data revolution comes with risks: Will SDG reporting consume already-limited government statistical capacity? Will “focus on